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arXiv 2102.03702cs.HC
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支持偶然性:定性分析中人类与人工智能合作的机遇和挑战

Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis

Jialun Aaron Jiang, Kandrea Wade, Casey Fiesler, Jed R. Brubaker

摘要:

定性归纳方法因其能够生成性地发现深入的、情境化的见解而广泛用于CSCW和人机交互研究,但这些固有的手动和人力资源密集型过程对于分析大型语料库通常是不可行的。研究人员对如何将定性方法应用于“大”数据问题越来越感兴趣,希望从更大量的数据中获得更普遍的结果,同时保持定性方法的深度和丰富性。在本文中,我们描述了一项关于定性研究人员的工作实践及其挑战的研究,着眼于这是否是一个适合人类与人工智能合作的领域,以及成功的合作可能需要什么。我们的研究结果描述了参与者的不同方法实践和微妙的协作动态,并确定了他们可能从基于人工智能的工具中受益的领域。虽然参与者强调了定性归纳分析的混乱和不确定性,但他们仍然希望在这个过程中拥有完全的代理权,并认为人工智能不应该干预。我们的研究在定性分析的背景下对人类与人工智能协作中的任务可委托性进行了深入的研究,并为尊重意外发现、人类代理和模糊性的人工智能援助的设计提供了方向。

英文摘要:

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for analyzing large corpora. Researchers have been increasingly interested in ways to apply qualitative methods to "big" data problems, hoping to achieve more generalizable results from larger amounts of data while preserving the depth and richness of qualitative methods. In this paper, we describe a study of qualitative researchers' work practices and their challenges, with an eye towards whether this is an appropriate domain for human-AI collaboration and what successful collaborations might entail. Our findings characterize participants' diverse methodological practices and nuanced collaboration dynamics, and identify areas where they might benefit from AI-based tools. While participants highlight the messiness and uncertainty of qualitative inductive analysis, they still want full agency over the process and believe that AI should not interfere. Our study provides a deep investigation of task delegability in human-AI collaboration in the context of qualitative analysis, and offers directions for the design of AI assistance that honor serendipity, human agency, and ambiguity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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